Figure REF  gives the overall architecture of BCNet for addressing occlusion in instance segmentation.
Following typical models [1]}, [2]} for instance segmentation, our model has three parts:
(1) Backbone [3]} with FPN [4]} for ROI feature extraction;
(2) Object detection head in charge of predicting bounding boxes
as instance proposals. We employ FCOS [5]} as the object detector owing to its anchor-free efficiency though our method is flexible and can deploy any existing fully supervised object detectors [6]}, [7]}, [8]};
(3) The occlusion-aware mask head, BCNet, uses bilayer GCN structure for decoupling overlapping relations and segments the instance proposals obtained from the object detection branch.
BCNet reformulates the traditional class-agnostic segmentation as two complementary tasks: occluder modeling using the first GCN and occludee prediction with the second GCN, where the auxiliary predictions from the first GCN provide rich occlusion cues, such as shape and positions of occluding regions, to guide target (occludee) object segmentation.
